Proceedings of the Australasian Joint Conference on Artificial Intelligence - Workshops最新文献

Prediction of Dissolved Oxygen from pH and Water Temperature in Aquaculture Prawn Ponds 水产对虾池溶解氧的pH和水温预测

Proceedings of the Australasian Joint Conference on Artificial Intelligence - Workshops Pub Date : 2018-12-11 DOI: 10.1145/3314487.3314488

J. Dabrowski, Ashfaqur Rahman, A. George

引用次数: 14

Ground-based Tree Image Data Mining for Phenology Research 基于地面的树木图像数据挖掘用于物候研究

Proceedings of the Australasian Joint Conference on Artificial Intelligence - Workshops Pub Date : 2018-12-11 DOI: 10.1145/3314487.3314489

Weixi Mao, Xiaobin Hua, Xiaohang Liu, Jeremiah D. Deng, R. Ohlemüller

{"title":"Ground-based Tree Image Data Mining for Phenology Research","authors":"Weixi Mao, Xiaobin Hua, Xiaohang Liu, Jeremiah D. Deng, R. Ohlemüller","doi":"10.1145/3314487.3314489","DOIUrl":"https://doi.org/10.1145/3314487.3314489","url":null,"abstract":"Previous phenological image analysis works use mostly close-up images or satellite remote sensing images. In this paper we employ ground-based tree images to investigate two related research tasks: colouring trends analysis and tree categorization. In the first task, we extracted a few colour features of image regions of three tree types: exotic deciduous, native non-deciduous, and native deciduous, from photographs taken in a period from spring to summer, to find out whether there are distinguishable colouring trends in different seasons and in different tree types. Statistical tests are carried out to verify the significance of our findings. In the tree categorization work, we examine possible image features that can be utilised for classifying trees into three types. Various classification schemes are explored, including using individual classifiers as well as classifier ensembles such as random forests. The colour trend analysis shows that the two native species have increasingly similar greening and hue from spring to summer, while the two deciduous types have similar change patterns matching their leaf growth trends. The tree categorization result shows that the highest F1 score is over 71% achieved by SVM and Random Forests classifiers. The HSV features are the most effective features for classification.","PeriodicalId":437653,"journal":{"name":"Proceedings of the Australasian Joint Conference on Artificial Intelligence - Workshops","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130616169","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Proceedings of the Australasian Joint Conference on Artificial Intelligence - Workshops 澳大利亚人工智能联合会议论文集-研讨会

Proceedings of the Australasian Joint Conference on Artificial Intelligence - Workshops Pub Date : 2018-12-11 DOI: 10.1145/3314487

引用次数: 0

Partial Solutions for Some Current Challenges in Stream Mining 当前流挖掘挑战的部分解决方案

Proceedings of the Australasian Joint Conference on Artificial Intelligence - Workshops Pub Date : 2018-12-11 DOI: 10.1145/3314487.3314492

B. Pfahringer

引用次数: 0

A Hybrid Load Balance Method Using Evolutionary Computing 一种基于进化计算的混合负载平衡方法

Proceedings of the Australasian Joint Conference on Artificial Intelligence - Workshops Pub Date : 2018-12-11 DOI: 10.1145/3314487.3314490

Xin Chi, Jia Yao, Huiming Yu

{"title":"A Hybrid Load Balance Method Using Evolutionary Computing","authors":"Xin Chi, Jia Yao, Huiming Yu","doi":"10.1145/3314487.3314490","DOIUrl":"https://doi.org/10.1145/3314487.3314490","url":null,"abstract":"Nginx is a commonly used and free open-source web server that is used as a reverse proxy server, load balancer and HTTP cache. It consumes less memory and can handle more clients with less number of processes. Nginx provides users with five predefined load balancing algorithms. However, most of these algorithms are static and some of the load balancing rules are inefficient. In order to make the load of a cluster more stable under high concurrent requests, we developed a Dynamic Load Balancing (DLB) algorithm that uses Nginx as a network security control panel to provide load balancing for a cluster of backend servers. The DLB algorithm is based on the weighted round robin module of Nginx, Logistic Regression and Maximum Likelihood Estimation (MLE) algorithm. It handles the situation of high concurrent requests and reduces the probability of omitted or under-reported incident and status. We also propose a Hybrid Load Balance Method (HLBM) that incorporates the DLB algorithm and evolutionary computing to further improve the performance. We have conducted limited experiment by using dynamic load balancing algorithm. We will complete to develop the Hybrid Load Balance method and conduct experiments for HLBM.","PeriodicalId":437653,"journal":{"name":"Proceedings of the Australasian Joint Conference on Artificial Intelligence - Workshops","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121802172","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

ECG Heartbeat Classification: An Exploratory Study 心电心跳分类:一项探索性研究

Proceedings of the Australasian Joint Conference on Artificial Intelligence - Workshops Pub Date : 2018-12-11 DOI: 10.1145/3314487.3314491

Loren Kersey, Katinya Lilly, Noel Park

引用次数: 2